Comparison Between Traditional Methods and the EB SPF Method for Identifying High Crash Risk Intersections

Transcription

1 Comparison Between Traditional Methods and the EB SPF Method for Identifying High Crash Risk Intersections In Kyu Lim Ph.D., P.E. CO TED 203 VASITE Annual Meeting

2 Outline.. Roadway Safety Management Process Overview of Network Screening Methods Research Motivation and Scopes Analysis and Results Conclusions

3 Roadway Safety Management Process Network Screening Diagnosis Select Countermeasures Safety Effectiveness Evaluation Prioritize Projects Economic Appraisal

4 Network Screening Purpose For reviewing a transportation network to identify and rank sites needed potential safety improvements Screening Methods Crash Frequency (CF) Crash Rate (CR) Rate Quality Control (RQC) Equivalent PropertyDamage Only (EPDO) Traditional Methods Empirical Bayes Safety Performance Functions (EB SPFs)

5 Screening Methods Traditional Crash Frequency y( (CF) CF i # Crash Strengths Simple Weaknesses Does not account for traffic volume No threshold 4 Does not account for RTM bias Crash Frequency AADT

6 Regression To the Mean (RTM) Observed Cra ash Frequenc cy Safety Improvement Project Does this actual effectiveness due to the treatment? No!! Perceived effectiveness of treatment Years

7 Regression To the Mean (RTM) Safety Improvement Project Observed Cra ash Frequenc cy RTM Reduction Perceived effectiveness of treatment Actual Reduction due to Treatment Years

8 Screening Methods Traditional Crash Rate (CR) CR i # Crash MEV i AADT MEV i ( n) (365),000,000 Strengths Simple Account for AADT 4 Crash Frequency Weaknesses No comparisons with different AADT Over estimation on low volume sites No threshold Does not account for RTM bias AADT

9 Screening Methods Traditional Rate Quality Control (RQC) CCR i ACR Strengths i z Critical Ratio ACR MEV CR i CCR i i 2 MEV i Cr rash Rate Establish a threshold for comparison Consider variance in crash data Reduces exaggerated effect of sites with low volumes Weaknesses Does not account for RTM bias CR CR Avg. CR CR Avg. CR CCR

10 Screening Methods Traditional EPDO EPDO i ws # Crashs, w s = weight factor for severity level s i Strengths Simple Considers crash severity Weaknesses Does not account for traffic volume No threshold Over estimate sites with severe crash Does not account for RTM bias Example Sites Crash hseverity Fatal Injury PDO # Crash EPDO Score Site Site Site

11 Screening Methods HSM Empirical Bayes Safety Performance Functions (EB SPFs). Safety Performance Functions (SPFs) SPFs are regression equations that estimate crash frequency as a function of annual average daily traffic (AADT) SPF Crash Frequency expa bln AADT

12 Screening Methods HSM Empirical Bayes Safety Performance Functions (EB SPFs) 2. Empirical Bayes EB is procedures for statistical inference in which the prior distribution is estimated fromthe data 3. EB SPFs EB SPFs use a weight factor, which is a function of the SPF over dispersion parameter, to combined the two estimates into a weighted average N expected w N SPF ( w ) ( N observed w k N SPF k = over-dispersion parameter from the associated SPF

13 Screening Methods HSM EB SPFs Strengths N expected w NSPF w) w k N SPF k = over-dispersion parameter from the associated SPF ( N observed Account for RTM bias Account for traffic volume Provide a threshold Weaknesses Requires more data included crash, roadway inventory and traffic data Requires SPFs calibrated to local conditions PSI i N expected, i N SPF, i

14 How many State use the EB SPF in U.S.? US? Only 2 3 State transportation agencies partially implemented, Over 90% of transportation agencies continuously use the traditional network screening methods Why? Lack of data Limitation of data access, compile and/or manipulation

15 Research Motivation and Scope Motivation Compare traditional methods for identifying high crash risk locations against the EB SPF Method Propose most reliable traditional network screening methods as the results of the EB SPF Scopes Four traditional methods included () Crash Frequency, (2) Crash Rate (CR), (3) Rate Quality Control and (4) Equivalent Property Damage Only (EPDO) Analysis is limited for four leg intersections categorized by area (urban/rural) andtraffic controldevice (traffic signal/two way waystop)

16 Data and Virginia SPFs Data Source Virginia SPFs VDOT Oracle db, called HTRIS (Highway Traffic Records Information System) Consists of 0 sub systems included crash, roadway inventory and traffic Since 2008, VDOT contributes efforts to develop Virginia SPFs with VCTIR VA SPF Development Status Two lane highway h completed ltd Intersection completed Multilane highway final review Freeway final review

17 Data Collection In HTRIS,,670 intersections under 4 categories as below were extracted using SQL.

18 Analysis and Results Distribution of,670 intersection PSIs using the EB SPF

19 Analysis and Results (cont) Scatter Plots of output values

20 Comparison Measures () Pearson Correlation Coefficient () (r) Measure Crash Frequency Crash Rate EPDO RQC EB-SPF (All) a EB-SPF (PSI > 0) b a All: total intersections (n =,670). b PSI > 0: positive PSI intersections (n = 842).

21 Comparison Measures (2) 2 Correct and false identification percentage of selecting top %, 5% and 0% hot spots

22 Comparison Measures (3) 3 Rank based Mean Absolute Error (MAE) MAE(Rank) n Rank(EB SPF) i Rank(T) n i i MAE(Rank) = mean absolute error in ranks Rank(EB SPF) = rankof location I based on the PSIfromthe EB SPFmethod Rank(T) = rank of location I based on performance measure from traditional methods n = number of locations varying depending on the specified top percent (, 5, and 0%)

23 Comparison Measures (3) 3 Rank based Mean Absolute Error (MAE)

24 Conclusions The Crash Rate (CR) method performed poorly in identifying the top, 5, and 0% of hot spots. Thus, crash rate method does not recommend to identifying hot spots. The Crash Frequency (CF) method performed the best in identifying the top % but false identification increased at the top 5 and 0%. The Rate Quality Control (RQC) method performed the best in top 5 and 0% and no false identification in the all three levels of categories.

25 Conclusions The EB SPF method recommends to use for identifying high crash intersections whenever it is feasible.

26 Information Thisresearch paper willbe pressed ontransportation Research Record (TRR) in 204. Any Technical Assistant for the EB SPF, please contact: In-Kyu Lim, Ph.D., P.E. Senior Highway Safety Engineer CO-TED Young-Jun Kweon, Ph.D., P.E. Research Scientist VCTIR

27 Thank You

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